CN205003133U - Mix gas ingredient detection device based on neural network - Google Patents
Mix gas ingredient detection device based on neural network Download PDFInfo
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- CN205003133U CN205003133U CN201520698976.0U CN201520698976U CN205003133U CN 205003133 U CN205003133 U CN 205003133U CN 201520698976 U CN201520698976 U CN 201520698976U CN 205003133 U CN205003133 U CN 205003133U
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Abstract
The utility model relates to a mix gas ingredient detection device based on neural network, including following device: (1 )Sensor matrix circuit part, (2 )The temperature detects the feedback part, (3 )The liquid crystal display part, (4 )The match is based on the single chip microcomputer control part of the multiple neural network optimization algorithm of matlab emulation, (5 )By the transformer, the power unit that rectifier bridge and LM7805 give first place to and constitute, (6 ) single chip microcomputer control part based on MSP430G2553, sensor matrix circuit subtotal feeds back the single chip microcomputer control part based on MSP430G2553 by the some information monitoring of temperature detection feedback that DS18B20 constitutes, the power unit who is simultaneously given first place to and constituted by transformer, rectifier bridge and LM7805 for whole circuit provides the power, optimizes the algorithm through predetermined neural network and calculates in the singlechip to the LCD12864 liquid crystal display part shows through being real -time.
Description
Technical field
A kind of mixed gas composition pick-up unit based on neural network of the utility model, particularly with the peculiar pick-up unit of sensor matrices and neural network algorithm.
Background technology
No matter multi-sensor information fusion technology is paid close attention to widely in military field or all receive in civil field in recent years; Traditional method first various gas componant is separated, and then detects respectively with the sensor being suitable for each gas respectively; Utilize infrared gas analyzer and gas imaging analysis instrument can do to test more accurately to gas componant and concentration, but its principle of work, device are complicated, environment for use requires high, and volume is large, expensive, does not have promotional value; The Multi-Gas Quantitative Analysis system adopting gas sensor matrix and neural network filter combine with technique to be formed is the quantitative detection utilizing the higher-dimension response model of sensor matrices to mixed gas to realize mixed gas; And wherein the choosing of sensor matrices, principal element that the preprocess method of sensor signal, the structural parameters of neural network and measurement environment are influential system performance.
This research for the existing Summary on research results of mixed gas context of detection research and comparative analysis mainly for the selection of neural network and optimum configurations, has and studies reference value and business potential value very well.
Summary of the invention
The circuit theory providing gas-detecting device of the present utility model is as follows:
The utility model relates to a kind of mixed gas composition pick-up unit based on neural network, it is characterized in that comprising with lower device: the singlechip control part of the multiple optimum algorithm of multi-layer neural network that (1) sensor matrices circuit part, (2) temperature detection feedback fraction, (3) liquid-crystal display section, (4) matching emulate based on Matlab, (5) by transformer, rectifier bridge and LM7805 be main formed power unit, (6) based on the singlechip control part of MSP430G2553.
Described mixed gas pick-up unit adopts single-chip microcomputer MSP430G2553 control circuit and key circuit and peripheral circuit thereof to realize the display of the composition of mixed gas, gas content is detected by sensor matrices, data are imported into single-chip microcomputer MSP430G2553, multiple optimum algorithm of multi-layer neural network is obtained based on Matlab emulation, preset a kind of optimum algorithm of multi-layer neural network, calculated in single-chip microcomputer by the optimum algorithm of multi-layer neural network preset, the optimization to parameter is realized by temperature feedback, obtain actual ratio, show in real time eventually through LCD12864.
Accompanying drawing explanation
Fig. 1 is utility model works theory diagram;
Fig. 2 is the utility model main loop circuit figure;
Fig. 3 is the utility model algorithm principle figure.
Embodiment
According to above-mentioned actual accompanying drawing, describe the embodiment of the utility model mixed gas pick-up unit in detail.
Wherein Fig. 1 is the control block diagram of whole device, described device comprises (1) sensor matrices circuit part, (2) the temperature detection feedback fraction be made up of DS18B20, (3) LCD12864 liquid-crystal display section, (4) singlechip control part of multiple optimum algorithm of multi-layer neural network that emulates based on Matlab of matching, (5) by transformer, rectifier bridge and LM7805 are the power unit of main formation, (6) based on the singlechip control part of MSP430G2553, sensor matrices circuit part and the temperature detection feedback fraction Information Monitoring be made up of DS18B20 feed back to the singlechip control part based on MSP430G2553, be simultaneously the main power unit formed by transformer, rectifier bridge and LM7805, for whole circuit provides power supply, calculated in single-chip microcomputer by the optimum algorithm of multi-layer neural network preset, and shown in real time by LCD12864 liquid-crystal display section.
Fig. 2 is main loop circuit figure, comprise the connection of single-chip minimum system and sensor matrices is connected with single-chip processor i/o mouth and liquid crystal and single-chip microcomputer connecting circuit, sensor image data, by I/O mouth, data are passed to single-chip microcomputer, multiple optimum algorithm of multi-layer neural network is obtained based on Matlab emulation, preset a kind of optimum algorithm of multi-layer neural network, calculated in single-chip microcomputer by the optimum algorithm of multi-layer neural network preset, after single-chip microcomputer process, obtain gas actual ratio, data are shown on 12864 liquid crystal.
Fig. 3 is the concrete optimization figure of this kind of algorithm, comprises the interconnected relationship between input layer, hidden layer, output layer.
Claims (2)
1. one kind comprises with lower part based on the mixed gas composition pick-up unit of neural network: (1) sensor matrices circuit part, (2) the temperature detection feedback fraction be made up of DS18B20, (3) LCD12864 liquid-crystal display section, (4) singlechip control part of multiple optimum algorithm of multi-layer neural network that emulates based on Matlab of matching, (5) by transformer, rectifier bridge and LM7805 are the power unit of main formation, (6) based on the singlechip control part of MSP430G2553, the temperature detection feedback fraction Information Monitoring that it is characterized in that sensor matrices circuit part and be made up of DS18B20 feeds back to the singlechip control part based on MSP430G2553, be simultaneously the main power unit formed by transformer, rectifier bridge and LM7805, for whole circuit provides power supply, calculated in single-chip microcomputer by the optimum algorithm of multi-layer neural network preset, and shown in real time by LCD12864 liquid-crystal display section.
2. a kind of mixed gas composition pick-up unit based on neural network according to claim 1, it is characterized in that sensor image data, by I/O mouth, data are passed to single-chip microcomputer, multiple optimum algorithm of multi-layer neural network is obtained based on Matlab emulation, preset a kind of optimum algorithm of multi-layer neural network, calculated in single-chip microcomputer by the optimum algorithm of multi-layer neural network preset, and shown in real time by LCD12864 liquid-crystal display section; Be simultaneously the main power unit formed by transformer, rectifier bridge and LM7805, for whole circuit provides power supply.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201520698976.0U CN205003133U (en) | 2015-09-10 | 2015-09-10 | Mix gas ingredient detection device based on neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201520698976.0U CN205003133U (en) | 2015-09-10 | 2015-09-10 | Mix gas ingredient detection device based on neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN205003133U true CN205003133U (en) | 2016-01-27 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201520698976.0U Expired - Fee Related CN205003133U (en) | 2015-09-10 | 2015-09-10 | Mix gas ingredient detection device based on neural network |
Country Status (1)
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| CN (1) | CN205003133U (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105823856A (en) * | 2016-05-03 | 2016-08-03 | 北京英视睿达科技有限公司 | Air quality monitoring method based on multisensor fusion |
-
2015
- 2015-09-10 CN CN201520698976.0U patent/CN205003133U/en not_active Expired - Fee Related
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105823856A (en) * | 2016-05-03 | 2016-08-03 | 北京英视睿达科技有限公司 | Air quality monitoring method based on multisensor fusion |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160127 Termination date: 20160910 |